AI anomaly correlation

log-first modelslog evidence extractionunsupervised correlationsemantic anomalytransformer-based detection

Logs come first, because they are the foundational data model for telemetry: metrics and traces are shapes of the same stream. AI and ML models detect slow-moving, cross-domain anomalies across your stack, from silicon to semantic, 100% unsupervised and without pre-configured rules. FFWD correlates them to surface root causes that siloed, metrics-centric tools miss.

Complex systems are cross-layer, cross-domain, cross-stack

Splunk sees security logs. Datadog sees APM traces. Network tools see packets. Each one alerts inside its own domain and misses everything between. The hardest problems are causal chains that propagate vertically through layers and laterally across stacks.

FFWD sees them all simultaneously because the pipeline sits at the aggregation point where telemetry from every layer and every domain converges.

Unsupervised marker auto-discovery, auto-extraction and auto-evaluation

FFWD continuously discovers and extracts markers from raw logs and metrics: features that describe your system’s operational state. An AI/ML toolbox evaluates every marker for anomalies in real time.

AI / ML toolbox

Scanning

Drift Detection

Catches slow statistical drift in numeric streams that thresholds will miss.

Explicit marker examples

CPU temperature Error codes Response times Throughput rates

Implicit marker examples

Log structure patterns Event sequences Semantic fingerprints Rate profiles

Root-cause advisory over MCP

Anomaly findings, journals and raw telemetry go directly to your AI agents, on one data layer that any MCP-compatible agent can query.

Natural-language root-cause advisory

FFWD synthesises anomaly findings into natural-language reports, powered by your LLM of choice: Claude, GPT, Gemini, Grok or on-prem.

Each report covers symptoms detected, probable root causes, remedies, and the specific symptomatic log lines as evidence.

Built-in MCP server

FFWD’s built-in MCP server exposes anomaly data, correlation reports, journals, and symptomatic logs directly to AI agents.

MCP also exposes raw logs and metrics for free-form queries, so AI agents can dig deeper, run their own analysis and navigate correlations conversationally, without a query language.

Any LLM, any MCP agent

Claude, GPT, Gemini, Grok, Copilot, on-prem.

ReBAC access control

Multi-tenant.

Within your perimeter

No SaaS data egress.

Private deployment

FFWD Anomaly Correlation runs entirely within your environment: on-premises, in a private cloud or air-gapped. Your telemetry never leaves your security perimeter, there are no SaaS dependencies, and the AI and ML models train and run on your infrastructure.

Multi-tenant architecture lets you run FFWD as private SaaS, serving multiple business units from a single deployment with full data isolation.